Overview

Dataset statistics

Number of variables22
Number of observations901
Missing cells1425
Missing cells (%)7.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory155.0 KiB
Average record size in memory176.1 B

Variable types

Numeric11
Categorical11

Alerts

trestbps is highly correlated with trestbpdHigh correlation
met is highly correlated with chol and 1 other fieldsHigh correlation
trestbpd is highly correlated with trestbpsHigh correlation
rldv5e is highly correlated with restecg and 1 other fieldsHigh correlation
chol is highly correlated with met and 1 other fieldsHigh correlation
cp is highly correlated with exangHigh correlation
restecg is highly correlated with rldv5eHigh correlation
pro is highly correlated with datasetHigh correlation
thalach is highly correlated with thalrest and 1 other fieldsHigh correlation
thalrest is highly correlated with thalachHigh correlation
tpeakbps is highly correlated with xhypoHigh correlation
exang is highly correlated with cp and 2 other fieldsHigh correlation
xhypo is highly correlated with tpeakbpsHigh correlation
oldpeak is highly correlated with exang and 1 other fieldsHigh correlation
num is highly correlated with oldpeakHigh correlation
dataset is highly correlated with chol and 3 other fieldsHigh correlation
trestbps has 61 (6.8%) missing values Missing
htn has 36 (4.0%) missing values Missing
chol has 32 (3.6%) missing values Missing
smoke has 389 (43.2%) missing values Missing
fbs has 92 (10.2%) missing values Missing
pro has 65 (7.2%) missing values Missing
met has 107 (11.9%) missing values Missing
thalach has 57 (6.3%) missing values Missing
thalrest has 58 (6.4%) missing values Missing
tpeakbps has 65 (7.2%) missing values Missing
tpeakbpd has 65 (7.2%) missing values Missing
trestbpd has 61 (6.8%) missing values Missing
exang has 57 (6.3%) missing values Missing
xhypo has 60 (6.7%) missing values Missing
oldpeak has 64 (7.1%) missing values Missing
rldv5e has 144 (16.0%) missing values Missing
chol has 172 (19.1%) zeros Zeros
oldpeak has 362 (40.2%) zeros Zeros

Reproduction

Analysis started2022-10-17 05:21:34.204127
Analysis finished2022-10-17 05:21:47.128704
Duration12.92 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

age
Real number (ℝ≥0)

Distinct50
Distinct (%)5.6%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean53.48053393
Minimum28
Maximum77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2022-10-17T07:21:47.190275image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile37
Q147
median54
Q360
95-th percentile68
Maximum77
Range49
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.435893735
Coefficient of variation (CV)0.1764360421
Kurtosis-0.3806810233
Mean53.48053393
Median Absolute Deviation (MAD)7
Skewness-0.1831893834
Sum48079
Variance89.03609058
MonotonicityNot monotonic
2022-10-17T07:21:47.296087image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5451
 
5.7%
5840
 
4.4%
5539
 
4.3%
5236
 
4.0%
5636
 
4.0%
6235
 
3.9%
5735
 
3.9%
5135
 
3.9%
5934
 
3.8%
5333
 
3.7%
Other values (40)525
58.3%
ValueCountFrequency (%)
281
 
0.1%
293
 
0.3%
301
 
0.1%
312
 
0.2%
325
0.6%
332
 
0.2%
347
0.8%
3510
1.1%
366
0.7%
3711
1.2%
ValueCountFrequency (%)
772
 
0.2%
762
 
0.2%
753
 
0.3%
747
0.8%
731
 
0.1%
724
 
0.4%
715
 
0.6%
707
0.8%
6913
1.4%
689
1.0%

sex
Categorical

Distinct2
Distinct (%)0.2%
Missing2
Missing (%)0.2%
Memory size7.2 KiB
1.0
711 
0.0
188 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2697
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row1.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0711
78.9%
0.0188
 
20.9%
(Missing)2
 
0.2%

Length

2022-10-17T07:21:47.374621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:21:47.443573image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0711
79.1%
0.0188
 
20.9%

Most occurring characters

ValueCountFrequency (%)
01087
40.3%
.899
33.3%
1711
26.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1798
66.7%
Other Punctuation899
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01087
60.5%
1711
39.5%
Other Punctuation
ValueCountFrequency (%)
.899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2697
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01087
40.3%
.899
33.3%
1711
26.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII2697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01087
40.3%
.899
33.3%
1711
26.4%

cp
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing2
Missing (%)0.2%
Memory size7.2 KiB
4.0
485 
3.0
202 
2.0
167 
1.0
 
45

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2697
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row3.0
3rd row2.0
4th row4.0
5th row3.0

Common Values

ValueCountFrequency (%)
4.0485
53.8%
3.0202
22.4%
2.0167
 
18.5%
1.045
 
5.0%
(Missing)2
 
0.2%

Length

2022-10-17T07:21:47.506474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:21:47.579953image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
4.0485
53.9%
3.0202
22.5%
2.0167
 
18.6%
1.045
 
5.0%

Most occurring characters

ValueCountFrequency (%)
.899
33.3%
0899
33.3%
4485
18.0%
3202
 
7.5%
2167
 
6.2%
145
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1798
66.7%
Other Punctuation899
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0899
50.0%
4485
27.0%
3202
 
11.2%
2167
 
9.3%
145
 
2.5%
Other Punctuation
ValueCountFrequency (%)
.899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2697
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.899
33.3%
0899
33.3%
4485
18.0%
3202
 
7.5%
2167
 
6.2%
145
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.899
33.3%
0899
33.3%
4485
18.0%
3202
 
7.5%
2167
 
6.2%
145
 
1.7%

trestbps
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct60
Distinct (%)7.1%
Missing61
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean132.1011905
Minimum0
Maximum200
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2022-10-17T07:21:47.659971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile105
Q1120
median130
Q3140
95-th percentile160
Maximum200
Range200
Interquartile range (IQR)20

Descriptive statistics

Standard deviation19.15112717
Coefficient of variation (CV)0.1449731611
Kurtosis2.968612092
Mean132.1011905
Median Absolute Deviation (MAD)10
Skewness0.2075650027
Sum110965
Variance366.765672
MonotonicityNot monotonic
2022-10-17T07:21:47.754532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120128
14.2%
130112
12.4%
140100
 
11.1%
11058
 
6.4%
15056
 
6.2%
16050
 
5.5%
12528
 
3.1%
11519
 
2.1%
13518
 
2.0%
12816
 
1.8%
Other values (50)255
28.3%
(Missing)61
 
6.8%
ValueCountFrequency (%)
01
 
0.1%
801
 
0.1%
921
 
0.1%
942
 
0.2%
956
 
0.7%
961
 
0.1%
981
 
0.1%
10015
1.7%
1011
 
0.1%
1023
 
0.3%
ValueCountFrequency (%)
2004
 
0.4%
1921
 
0.1%
1902
 
0.2%
1851
 
0.1%
18012
1.3%
1783
 
0.3%
1741
 
0.1%
1722
 
0.2%
17013
1.4%
1652
 
0.2%

htn
Categorical

MISSING

Distinct2
Distinct (%)0.2%
Missing36
Missing (%)4.0%
Memory size7.2 KiB
0.0
453 
1.0
412 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2595
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0453
50.3%
1.0412
45.7%
(Missing)36
 
4.0%

Length

2022-10-17T07:21:47.839041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:21:47.908317image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0453
52.4%
1.0412
47.6%

Most occurring characters

ValueCountFrequency (%)
01318
50.8%
.865
33.3%
1412
 
15.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1730
66.7%
Other Punctuation865
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01318
76.2%
1412
 
23.8%
Other Punctuation
ValueCountFrequency (%)
.865
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2595
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01318
50.8%
.865
33.3%
1412
 
15.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2595
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01318
50.8%
.865
33.3%
1412
 
15.9%

chol
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
ZEROS

Distinct213
Distinct (%)24.5%
Missing32
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean198.7594937
Minimum0
Maximum603
Zeros172
Zeros (%)19.1%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2022-10-17T07:21:47.978930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1175
median224
Q3269
95-th percentile334.2
Maximum603
Range603
Interquartile range (IQR)94

Descriptive statistics

Standard deviation111.8344148
Coefficient of variation (CV)0.5626620028
Kurtosis0.00684770048
Mean198.7594937
Median Absolute Deviation (MAD)46
Skewness-0.6049884266
Sum172722
Variance12506.93633
MonotonicityNot monotonic
2022-10-17T07:21:48.068889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0172
 
19.1%
25410
 
1.1%
2049
 
1.0%
2199
 
1.0%
2309
 
1.0%
2169
 
1.0%
2239
 
1.0%
2209
 
1.0%
2119
 
1.0%
2608
 
0.9%
Other values (203)616
68.4%
(Missing)32
 
3.6%
ValueCountFrequency (%)
0172
19.1%
851
 
0.1%
1002
 
0.2%
1171
 
0.1%
1261
 
0.1%
1291
 
0.1%
1321
 
0.1%
1391
 
0.1%
1411
 
0.1%
1421
 
0.1%
ValueCountFrequency (%)
6031
0.1%
5641
0.1%
5291
0.1%
5181
0.1%
4911
0.1%
4681
0.1%
4661
0.1%
4581
0.1%
4171
0.1%
4121
0.1%

smoke
Categorical

MISSING

Distinct2
Distinct (%)0.4%
Missing389
Missing (%)43.2%
Memory size7.2 KiB
1.0
286 
0.0
226 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1536
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0286
31.7%
0.0226
25.1%
(Missing)389
43.2%

Length

2022-10-17T07:21:48.146423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:21:48.208367image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0286
55.9%
0.0226
44.1%

Most occurring characters

ValueCountFrequency (%)
0738
48.0%
.512
33.3%
1286
 
18.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1024
66.7%
Other Punctuation512
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0738
72.1%
1286
 
27.9%
Other Punctuation
ValueCountFrequency (%)
.512
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1536
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0738
48.0%
.512
33.3%
1286
 
18.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1536
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0738
48.0%
.512
33.3%
1286
 
18.6%

fbs
Categorical

MISSING

Distinct2
Distinct (%)0.2%
Missing92
Missing (%)10.2%
Memory size7.2 KiB
0.0
674 
1.0
135 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2427
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0674
74.8%
1.0135
 
15.0%
(Missing)92
 
10.2%

Length

2022-10-17T07:21:48.262802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:21:48.337596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0674
83.3%
1.0135
 
16.7%

Most occurring characters

ValueCountFrequency (%)
01483
61.1%
.809
33.3%
1135
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1618
66.7%
Other Punctuation809
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01483
91.7%
1135
 
8.3%
Other Punctuation
ValueCountFrequency (%)
.809
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2427
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01483
61.1%
.809
33.3%
1135
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2427
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01483
61.1%
.809
33.3%
1135
 
5.6%

restecg
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)0.3%
Missing4
Missing (%)0.4%
Memory size7.2 KiB
0.0
538 
2.0
182 
1.0
177 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2691
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0538
59.7%
2.0182
 
20.2%
1.0177
 
19.6%
(Missing)4
 
0.4%

Length

2022-10-17T07:21:48.398809image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:21:48.466924image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0538
60.0%
2.0182
 
20.3%
1.0177
 
19.7%

Most occurring characters

ValueCountFrequency (%)
01435
53.3%
.897
33.3%
2182
 
6.8%
1177
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1794
66.7%
Other Punctuation897
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01435
80.0%
2182
 
10.1%
1177
 
9.9%
Other Punctuation
ValueCountFrequency (%)
.897
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2691
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01435
53.3%
.897
33.3%
2182
 
6.8%
1177
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2691
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01435
53.3%
.897
33.3%
2182
 
6.8%
1177
 
6.6%

pro
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing65
Missing (%)7.2%
Memory size7.2 KiB
0.0
692 
1.0
144 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2508
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0692
76.8%
1.0144
 
16.0%
(Missing)65
 
7.2%

Length

2022-10-17T07:21:48.533758image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:21:48.611149image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0692
82.8%
1.0144
 
17.2%

Most occurring characters

ValueCountFrequency (%)
01528
60.9%
.836
33.3%
1144
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1672
66.7%
Other Punctuation836
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01528
91.4%
1144
 
8.6%
Other Punctuation
ValueCountFrequency (%)
.836
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2508
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01528
60.9%
.836
33.3%
1144
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII2508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01528
60.9%
.836
33.3%
1144
 
5.7%

met
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct34
Distinct (%)4.3%
Missing107
Missing (%)11.9%
Infinite0
Infinite (%)0.0%
Mean16.48312343
Minimum2
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2022-10-17T07:21:48.950429image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median7
Q310
95-th percentile100
Maximum200
Range198
Interquartile range (IQR)5

Descriptive statistics

Standard deviation30.77280112
Coefficient of variation (CV)1.866927786
Kurtosis10.72913546
Mean16.48312343
Median Absolute Deviation (MAD)2
Skewness3.378510563
Sum13087.6
Variance946.9652886
MonotonicityNot monotonic
2022-10-17T07:21:49.050014image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
7107
11.9%
5106
11.8%
6102
11.3%
971
7.9%
1058
 
6.4%
454
 
6.0%
852
 
5.8%
331
 
3.4%
10031
 
3.4%
1327
 
3.0%
Other values (24)155
17.2%
(Missing)107
11.9%
ValueCountFrequency (%)
222
 
2.4%
2.51
 
0.1%
331
 
3.4%
3.51
 
0.1%
454
6.0%
4.51
 
0.1%
5106
11.8%
5.41
 
0.1%
5.81
 
0.1%
6102
11.3%
ValueCountFrequency (%)
2001
 
0.1%
15021
2.3%
1252
 
0.2%
10031
3.4%
757
 
0.8%
5011
 
1.2%
182
 
0.2%
172
 
0.2%
167
 
0.8%
153
 
0.3%

thalach
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct119
Distinct (%)14.1%
Missing57
Missing (%)6.3%
Infinite0
Infinite (%)0.0%
Mean137.2985782
Minimum60
Maximum202
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2022-10-17T07:21:49.149379image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile95
Q1120
median140
Q3157
95-th percentile178
Maximum202
Range142
Interquartile range (IQR)37

Descriptive statistics

Standard deviation25.96595906
Coefficient of variation (CV)0.1891203784
Kurtosis-0.4847315481
Mean137.2985782
Median Absolute Deviation (MAD)20
Skewness-0.1999350826
Sum115880
Variance674.23103
MonotonicityNot monotonic
2022-10-17T07:21:49.252024image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15042
 
4.7%
14040
 
4.4%
12035
 
3.9%
13029
 
3.2%
16026
 
2.9%
11021
 
2.3%
12520
 
2.2%
17020
 
2.2%
12216
 
1.8%
10014
 
1.6%
Other values (109)581
64.5%
(Missing)57
 
6.3%
ValueCountFrequency (%)
601
0.1%
631
0.1%
671
0.1%
691
0.1%
701
0.1%
711
0.1%
722
0.2%
731
0.1%
771
0.1%
781
0.1%
ValueCountFrequency (%)
2021
 
0.1%
1951
 
0.1%
1941
 
0.1%
1921
 
0.1%
1902
0.2%
1882
0.2%
1871
 
0.1%
1862
0.2%
1854
0.4%
1844
0.4%

thalrest
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct75
Distinct (%)8.9%
Missing58
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean75.48754448
Minimum37
Maximum139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2022-10-17T07:21:49.360397image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum37
5-th percentile55
Q165
median74
Q384
95-th percentile100
Maximum139
Range102
Interquartile range (IQR)19

Descriptive statistics

Standard deviation14.72796148
Coefficient of variation (CV)0.1951045246
Kurtosis0.7390791357
Mean75.48754448
Median Absolute Deviation (MAD)10
Skewness0.6366775286
Sum63636
Variance216.9128494
MonotonicityNot monotonic
2022-10-17T07:21:49.468856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7041
 
4.6%
7433
 
3.7%
8030
 
3.3%
6830
 
3.3%
7527
 
3.0%
7226
 
2.9%
6426
 
2.9%
7325
 
2.8%
8425
 
2.8%
7824
 
2.7%
Other values (65)556
61.7%
(Missing)58
 
6.4%
ValueCountFrequency (%)
371
 
0.1%
391
 
0.1%
401
 
0.1%
431
 
0.1%
441
 
0.1%
462
0.2%
471
 
0.1%
494
0.4%
504
0.4%
511
 
0.1%
ValueCountFrequency (%)
1391
 
0.1%
1341
 
0.1%
1253
0.3%
1241
 
0.1%
1203
0.3%
1191
 
0.1%
1161
 
0.1%
1152
 
0.2%
1122
 
0.2%
1106
0.7%

tpeakbps
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct74
Distinct (%)8.9%
Missing65
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean171.6411483
Minimum84
Maximum240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2022-10-17T07:21:49.579910image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum84
5-th percentile130
Q1155
median170
Q3190
95-th percentile220
Maximum240
Range156
Interquartile range (IQR)35

Descriptive statistics

Standard deviation25.73448825
Coefficient of variation (CV)0.1499319278
Kurtosis0.1626878493
Mean171.6411483
Median Absolute Deviation (MAD)18
Skewness0.04005466243
Sum143492
Variance662.2638856
MonotonicityNot monotonic
2022-10-17T07:21:49.677807image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18097
 
10.8%
16095
 
10.5%
17081
 
9.0%
19066
 
7.3%
20058
 
6.4%
15047
 
5.2%
14040
 
4.4%
22024
 
2.7%
21021
 
2.3%
13018
 
2.0%
Other values (64)289
32.1%
(Missing)65
 
7.2%
ValueCountFrequency (%)
841
 
0.1%
901
 
0.1%
921
 
0.1%
982
 
0.2%
1001
 
0.1%
1104
0.4%
1121
 
0.1%
1151
 
0.1%
1161
 
0.1%
1209
1.0%
ValueCountFrequency (%)
2405
 
0.6%
2351
 
0.1%
2321
 
0.1%
23014
1.6%
2281
 
0.1%
2241
 
0.1%
22024
2.7%
2161
 
0.1%
2155
 
0.6%
21021
2.3%

tpeakbpd
Real number (ℝ≥0)

MISSING

Distinct51
Distinct (%)6.1%
Missing65
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean87.2930622
Minimum11
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2022-10-17T07:21:49.779299image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile65
Q180
median88
Q3100
95-th percentile110
Maximum134
Range123
Interquartile range (IQR)20

Descriptive statistics

Standard deviation14.73458597
Coefficient of variation (CV)0.1687944677
Kurtosis0.924026069
Mean87.2930622
Median Absolute Deviation (MAD)10
Skewness-0.1306504115
Sum72977
Variance217.1080237
MonotonicityNot monotonic
2022-10-17T07:21:49.892452image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80162
18.0%
90131
14.5%
100114
12.7%
7059
 
6.5%
11043
 
4.8%
9528
 
3.1%
8525
 
2.8%
6023
 
2.6%
7523
 
2.6%
7822
 
2.4%
Other values (41)206
22.9%
(Missing)65
 
7.2%
ValueCountFrequency (%)
111
 
0.1%
261
 
0.1%
402
 
0.2%
451
 
0.1%
502
 
0.2%
551
 
0.1%
562
 
0.2%
583
 
0.3%
6023
2.6%
623
 
0.3%
ValueCountFrequency (%)
1341
 
0.1%
1302
 
0.2%
12015
 
1.7%
1184
 
0.4%
1162
 
0.2%
1158
 
0.9%
1142
 
0.2%
1121
 
0.1%
11043
4.8%
1082
 
0.2%

trestbpd
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct34
Distinct (%)4.0%
Missing61
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean83.52380952
Minimum0
Maximum120
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2022-10-17T07:21:49.993322image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile70
Q180
median80
Q390
95-th percentile100
Maximum120
Range120
Interquartile range (IQR)10

Descriptive statistics

Standard deviation10.25256291
Coefficient of variation (CV)0.1227501831
Kurtosis4.985306216
Mean83.52380952
Median Absolute Deviation (MAD)8
Skewness-0.5407270172
Sum70160
Variance105.1150463
MonotonicityNot monotonic
2022-10-17T07:21:50.089303image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
80258
28.6%
90158
17.5%
7088
 
9.8%
10064
 
7.1%
8542
 
4.7%
7824
 
2.7%
9523
 
2.6%
7521
 
2.3%
8215
 
1.7%
8814
 
1.6%
Other values (24)133
14.8%
(Missing)61
 
6.8%
ValueCountFrequency (%)
01
 
0.1%
502
 
0.2%
581
 
0.1%
6012
 
1.3%
644
 
0.4%
656
 
0.7%
661
 
0.1%
684
 
0.4%
7088
9.8%
728
 
0.9%
ValueCountFrequency (%)
1201
 
0.1%
1107
 
0.8%
1062
 
0.2%
1055
 
0.6%
1041
 
0.1%
1021
 
0.1%
10064
7.1%
9812
 
1.3%
967
 
0.8%
9523
 
2.6%

exang
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing57
Missing (%)6.3%
Memory size7.2 KiB
0.0
514 
1.0
330 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2532
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0514
57.0%
1.0330
36.6%
(Missing)57
 
6.3%

Length

2022-10-17T07:21:50.180023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:21:50.265658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0514
60.9%
1.0330
39.1%

Most occurring characters

ValueCountFrequency (%)
01358
53.6%
.844
33.3%
1330
 
13.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1688
66.7%
Other Punctuation844
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01358
80.5%
1330
 
19.5%
Other Punctuation
ValueCountFrequency (%)
.844
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2532
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01358
53.6%
.844
33.3%
1330
 
13.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII2532
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01358
53.6%
.844
33.3%
1330
 
13.0%

xhypo
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)0.2%
Missing60
Missing (%)6.7%
Memory size7.2 KiB
0.0
819 
1.0
 
22

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2523
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0819
90.9%
1.022
 
2.4%
(Missing)60
 
6.7%

Length

2022-10-17T07:21:50.336076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:21:50.415661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0819
97.4%
1.022
 
2.6%

Most occurring characters

ValueCountFrequency (%)
01660
65.8%
.841
33.3%
122
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1682
66.7%
Other Punctuation841
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01660
98.7%
122
 
1.3%
Other Punctuation
ValueCountFrequency (%)
.841
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2523
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01660
65.8%
.841
33.3%
122
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII2523
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01660
65.8%
.841
33.3%
122
 
0.9%

oldpeak
Real number (ℝ)

HIGH CORRELATION
MISSING
ZEROS

Distinct52
Distinct (%)6.2%
Missing64
Missing (%)7.1%
Infinite0
Infinite (%)0.0%
Mean0.8704898447
Minimum-2.6
Maximum6.2
Zeros362
Zeros (%)40.2%
Negative12
Negative (%)1.3%
Memory size7.2 KiB
2022-10-17T07:21:50.490523image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum-2.6
5-th percentile0
Q10
median0.5
Q31.5
95-th percentile3
Maximum6.2
Range8.8
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.080548494
Coefficient of variation (CV)1.241310855
Kurtosis1.147635071
Mean0.8704898447
Median Absolute Deviation (MAD)0.5
Skewness1.027733086
Sum728.6
Variance1.167585047
MonotonicityNot monotonic
2022-10-17T07:21:50.587835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0362
40.2%
182
 
9.1%
275
 
8.3%
1.548
 
5.3%
328
 
3.1%
0.519
 
2.1%
2.516
 
1.8%
1.415
 
1.7%
1.214
 
1.6%
1.614
 
1.6%
Other values (42)164
18.2%
(Missing)64
 
7.1%
ValueCountFrequency (%)
-2.61
0.1%
-21
0.1%
-1.51
0.1%
-1.11
0.1%
-12
0.2%
-0.91
0.1%
-0.81
0.1%
-0.71
0.1%
-0.52
0.2%
-0.11
0.1%
ValueCountFrequency (%)
6.21
 
0.1%
5.61
 
0.1%
51
 
0.1%
4.22
 
0.2%
47
0.8%
3.81
 
0.1%
3.71
 
0.1%
3.64
0.4%
3.52
 
0.2%
3.42
 
0.2%

rldv5e
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct141
Distinct (%)18.6%
Missing144
Missing (%)16.0%
Infinite0
Infinite (%)0.0%
Mean54.91413474
Minimum2
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.2 KiB
2022-10-17T07:21:50.688343image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q112
median19
Q3102
95-th percentile176
Maximum270
Range268
Interquartile range (IQR)90

Descriptive statistics

Standard deviation60.309425
Coefficient of variation (CV)1.098249572
Kurtosis0.2720851797
Mean54.91413474
Median Absolute Deviation (MAD)10
Skewness1.185535476
Sum41570
Variance3637.226744
MonotonicityNot monotonic
2022-10-17T07:21:50.791254image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2042
 
4.7%
1138
 
4.2%
1533
 
3.7%
1032
 
3.6%
1730
 
3.3%
1330
 
3.3%
927
 
3.0%
1826
 
2.9%
1226
 
2.9%
723
 
2.6%
Other values (131)450
49.9%
(Missing)144
 
16.0%
ValueCountFrequency (%)
21
 
0.1%
33
 
0.3%
47
 
0.8%
56
 
0.7%
618
2.0%
723
2.6%
823
2.6%
927
3.0%
1032
3.6%
1138
4.2%
ValueCountFrequency (%)
2701
 
0.1%
2531
 
0.1%
2521
 
0.1%
2401
 
0.1%
2311
 
0.1%
2302
0.2%
2271
 
0.1%
2251
 
0.1%
2221
 
0.1%
2203
0.3%

num
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.6%
Missing2
Missing (%)0.2%
Memory size7.2 KiB
0.0
404 
1.0
191 
3.0
132 
2.0
130 
4.0
42 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2697
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row3.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0404
44.8%
1.0191
21.2%
3.0132
 
14.7%
2.0130
 
14.4%
4.042
 
4.7%
(Missing)2
 
0.2%

Length

2022-10-17T07:21:50.876473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:21:50.964496image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0404
44.9%
1.0191
21.2%
3.0132
 
14.7%
2.0130
 
14.5%
4.042
 
4.7%

Most occurring characters

ValueCountFrequency (%)
01303
48.3%
.899
33.3%
1191
 
7.1%
3132
 
4.9%
2130
 
4.8%
442
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number1798
66.7%
Other Punctuation899
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
01303
72.5%
1191
 
10.6%
3132
 
7.3%
2130
 
7.2%
442
 
2.3%
Other Punctuation
ValueCountFrequency (%)
.899
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common2697
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
01303
48.3%
.899
33.3%
1191
 
7.1%
3132
 
4.9%
2130
 
4.8%
442
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII2697
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
01303
48.3%
.899
33.3%
1191
 
7.1%
3132
 
4.9%
2130
 
4.8%
442
 
1.6%

dataset
Categorical

HIGH CORRELATION

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size7.2 KiB
hungarian
295 
cleveland
282 
long-beach-va
201 
switzerland
123 

Length

Max length13
Median length9
Mean length10.16537181
Min length9

Characters and Unicode

Total characters9159
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhungarian
2nd rowhungarian
3rd rowhungarian
4th rowhungarian
5th rowhungarian

Common Values

ValueCountFrequency (%)
hungarian295
32.7%
cleveland282
31.3%
long-beach-va201
22.3%
switzerland123
13.7%

Length

2022-10-17T07:21:51.056142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-17T07:21:51.154590image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
hungarian295
32.7%
cleveland282
31.3%
long-beach-va201
22.3%
switzerland123
13.7%

Most occurring characters

ValueCountFrequency (%)
a1397
15.3%
n1196
13.1%
e888
9.7%
l888
9.7%
h496
 
5.4%
g496
 
5.4%
c483
 
5.3%
v483
 
5.3%
r418
 
4.6%
i418
 
4.6%
Other values (9)1996
21.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter8757
95.6%
Dash Punctuation402
 
4.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a1397
16.0%
n1196
13.7%
e888
10.1%
l888
10.1%
h496
 
5.7%
g496
 
5.7%
c483
 
5.5%
v483
 
5.5%
r418
 
4.8%
i418
 
4.8%
Other values (8)1594
18.2%
Dash Punctuation
ValueCountFrequency (%)
-402
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin8757
95.6%
Common402
 
4.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a1397
16.0%
n1196
13.7%
e888
10.1%
l888
10.1%
h496
 
5.7%
g496
 
5.7%
c483
 
5.5%
v483
 
5.5%
r418
 
4.8%
i418
 
4.8%
Other values (8)1594
18.2%
Common
ValueCountFrequency (%)
-402
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII9159
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a1397
15.3%
n1196
13.1%
e888
9.7%
l888
9.7%
h496
 
5.4%
g496
 
5.4%
c483
 
5.3%
v483
 
5.3%
r418
 
4.6%
i418
 
4.6%
Other values (9)1996
21.8%

Interactions

2022-10-17T07:21:45.375049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:35.079800image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:36.195796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:37.479740image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:38.408815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:39.399831image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:40.335002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:41.489057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:42.453744image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:43.416903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:44.286106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:45.446190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:35.343301image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:36.282971image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:37.573461image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:38.486816image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:39.485022image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:40.420652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:41.569575image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:42.551854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:43.509846image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:44.351584image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:45.526360image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:35.425385image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:36.378271image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:37.668657image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:38.579084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:39.571990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:40.507214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:41.668607image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:42.637536image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:43.589719image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:44.440425image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:45.606533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:35.510190image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:36.468421image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:37.747638image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:38.676499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:39.651019image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:40.580836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:41.749627image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:42.728835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:43.666771image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:44.521234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:45.690131image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:35.598210image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:36.557891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:37.829444image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:38.773656image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:39.740175image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:40.681162image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:41.840443image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:42.819601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:43.748802image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:44.606474image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:45.767765image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:35.686063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:36.648481image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:37.910031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:38.863490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:39.829446image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:40.769049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:41.926503image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:42.902525image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:43.828041image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:44.682156image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:45.837698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:35.766882image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:37.019933image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:37.996199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:38.951334image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:39.922229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:40.850594image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:42.017021image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:42.995140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:43.904403image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:44.758847image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:45.921230image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:35.852221image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:37.131438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:38.085711image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:39.042541image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:40.013769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:40.931741image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:42.105606image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:43.092674image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:43.989004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:44.840633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:45.998874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:35.935266image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:37.226195image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:38.164243image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:39.128755image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:40.097778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:41.257056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:42.191978image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:43.176381image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:44.070155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:44.912513image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:46.075076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:36.029316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:37.313339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:38.240855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:39.228277image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:40.180411image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:41.345983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:42.288333image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:43.268636image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:44.145788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:44.985216image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:46.149354image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:36.113693image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:37.397650image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:38.318530image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:39.311750image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:40.262546image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:41.415532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:42.369708image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:43.340872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:44.215639image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2022-10-17T07:21:45.056159image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2022-10-17T07:21:51.252532image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-17T07:21:51.450185image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-17T07:21:51.637543image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-17T07:21:51.812202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-17T07:21:51.938853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-17T07:21:46.287438image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-17T07:21:46.546973image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-10-17T07:21:46.758490image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-10-17T07:21:47.054726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

agesexcptrestbpshtncholsmokefbsrestecgprometthalachthalresttpeakbpstpeakbpdtrestbpdexangxhypooldpeakrldv5enumdataset
040.01.02.0140.00.0289.0NaN0.00.00.07.0172.086.0200.0110.086.00.00.00.020.00.0hungarian
149.00.03.0160.01.0180.0NaN0.00.00.07.0156.0100.0220.0106.090.00.00.01.013.01.0hungarian
237.01.02.0130.00.0283.0NaN0.01.00.05.098.058.0180.0100.080.00.00.00.014.00.0hungarian
348.00.04.0138.00.0214.0NaN0.00.00.04.0108.054.0210.0106.086.01.00.01.522.03.0hungarian
454.01.03.0150.00.0NaNNaN0.00.01.03.0122.074.0130.0100.090.00.01.00.09.00.0hungarian
539.01.03.0120.00.0339.0NaN0.00.00.08.0170.086.0198.0100.080.00.00.00.021.00.0hungarian
645.00.02.0130.00.0237.0NaN0.00.00.010.0170.090.0200.0106.084.00.00.00.011.00.0hungarian
754.01.02.0110.00.0208.0NaN0.00.00.07.0142.056.0220.070.070.00.00.00.011.00.0hungarian
837.01.04.0140.01.0207.0NaN0.00.00.07.0130.063.0190.0100.080.01.00.01.519.01.0hungarian
948.00.02.0120.00.0284.0NaN0.00.00.04.0120.072.0140.080.080.00.00.00.06.00.0hungarian

Last rows

agesexcptrestbpshtncholsmokefbsrestecgprometthalachthalresttpeakbpstpeakbpdtrestbpdexangxhypooldpeakrldv5enumdataset
89162.01.04.0160.01.0254.01.01.01.01.02.5108.069.0160.090.080.01.00.03.019.04.0long-beach-va
89253.01.04.0144.01.0300.00.01.01.00.05.0128.076.0150.0102.094.01.00.01.513.03.0long-beach-va
89362.01.04.0158.01.0170.01.00.01.00.08.0138.086.0202.098.090.01.00.00.022.01.0long-beach-va
89446.01.04.0134.01.0310.01.00.00.00.07.0126.088.0174.0114.090.00.00.00.07.02.0long-beach-va
89554.00.04.0127.00.0333.00.01.01.00.08.0154.083.0158.084.078.00.00.00.020.01.0long-beach-va
89662.01.01.0NaN0.0139.01.00.01.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0long-beach-va
89755.01.04.0122.01.0223.01.01.01.00.05.0100.074.0210.0100.070.00.00.00.04.02.0long-beach-va
89858.01.04.0NaN0.0385.00.01.02.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0long-beach-va
89962.01.02.0120.01.0254.00.00.02.00.07.093.067.0164.0110.080.01.00.00.017.01.0long-beach-va
900NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNlong-beach-va